Asserting The Security Restrictions Applicable To Images Posted By Users To Information Platforms

Abstract

It's becoming more difficult to maintain privacy in the age of social media, as seen by the recent rash of high-profile examples in which people have inadvertently released private information online. All of these incidents show why it's crucial to have user access management tools for freely available information. To address this requirement, we propose an Adaptive Privacy Policy Prediction (A3P) system that may provide users with guidance on how to organise their picture privacy settings. Here, we investigate if and how a user's privacy preferences may be revealed via their social network settings, image content, and metadata. Our two-tiered method takes into account the user's prior activity on the site to determine the most fitting privacy options for their future picture uploads. Our method employs a policy prediction algorithm to automatically build a policy for each newly submitted image, taking into consideration users' social qualities, and an image classification framework to find groups of photos that may be associated by similar rules. Rulemaking will evolve over time to accommodate shifting public attitudes towards personal data privacy. We provide the results of a large-scale analysis of more than 6,000 policies, demonstrating that our method achieves prediction accuracy of 93% or better.